OpenRaiser/Intern-Atlas huggingface dataset:
A Survey of Inductive Biases for Factorial Representation-Learning
Venue: arXiv cs.LG 2016
Year: 2016

With the resurgence of interest in neural networks, representation learning has re-emerged as a central focus in artificial intelligence. Representation learning refers to the discovery of useful encodings of data that make domain-relevant information explicit. Factorial representations identify underlying independent causal factors of variation in data. A factorial representation is compact and faithful, makes the causal factors explicit, and facilitates human interpretation of data. Factorial representations support a variety of applications, including the generation of novel examples, indexing and search, novelty detection, and transfer learning. This article surveys various constraints that encourage a learning algorithm to discover factorial representations. I dichotomize the constraints in terms of unsupervised and supervised inductive bias. Unsupervised inductive biases exploit assumptions about the environment, such as the statistical distribution of factor coefficients, assumptions about the perturbations a factor should be invariant to (e.g. a representation of an object can be invariant to rotation, translation or scaling), and assumptions about how factors are combined to synthesize an observation. Supervised inductive biases are constraints on the representations based on additional information connected to observations. Supervisory labels come in variety of types, which vary in how strongly they constrain the representation, how many factors are labeled, how many observations are labeled, and whether or not we know the associations between the constraints and the factors they are related to. This survey brings together a wide variety of models that all touch on the problem of learning factorial representations and lays out a framework for comparing these models based on the strengths of the underlying supervised and unsupervised inductive biases.
Modeling Trolling in Social Media Conversations
Venue: arXiv cs.CL 2016
Year: 2016

Social media websites, electronic newspapers and Internet forums allow visitors to leave comments for others to read and interact. This exchange is not free from participants with malicious intentions, who troll others by positing messages that are intended to be provocative, offensive, or menacing. With the goal of facilitating the computational modeling of trolling, we propose a trolling categorization that is novel in the sense that it allows comment-based analysis from both the trolls' and the responders' perspectives, characterizing these two perspectives using four aspects, namely, the troll's intention and his intention disclosure, as well as the responder's interpretation of the troll's intention and her response strategy. Using this categorization, we annotate and release a dataset containing excerpts of Reddit conversations involving suspected trolls and their interactions with other users. Finally, we identify the difficult-to-classify cases in our corpus and suggest potential solutions for them.
Minimum Critical Exponents for Palindromes
Venue: arXiv cs.FL 2016
Year: 2016

We determine the minimum possible critical exponent for all palindromes over finite alphabets.
A Stochastic Large Deformation Model for Computational Anatomy
Venue: arXiv cs.CV 2016
Year: 2016

In the study of shapes of human organs using computational anatomy, variations are found to arise from inter-subject anatomical differences, disease-specific effects, and measurement noise. This paper introduces a stochastic model for incorporating random variations into the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. By accounting for randomness in a particular setup which is crafted to fit the geometrical properties of LDDMM, we formulate the template estimation problem for landmarks with noise and give two methods for efficiently estimating the parameters of the noise fields from a prescribed data set. One method directly approximates the time evolution of the variance of each landmark by a finite set of differential equations, and the other is based on an Expectation-Maximisation algorithm. In the second method, the evaluation of the data likelihood is achieved without registering the landmarks, by applying bridge sampling using a stochastically perturbed version of the large deformation gradient flow algorithm. The method and the estimation algorithms are experimentally validated on synthetic examples and shape data of human corpora callosa.
Medical Image Synthesis with Context-Aware Generative Adversarial Networks
Venue: arXiv cs.CV 2016
Year: 2016

Computed tomography (CT) is critical for various clinical applications, e.g., radiotherapy treatment planning and also PET attenuation correction. However, CT exposes radiation during acquisition, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve any radiations. Therefore, recently, researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiotherapy planning. In this paper, we propose a data-driven approach to address this challenging problem. Specifically, we train a fully convolutional network to generate CT given an MR image. To better model the nonlinear relationship from MRI to CT and to produce more realistic images, we propose to use the adversarial training strategy and an image gradient difference loss function. We further apply AutoContext Model to implement a context-aware generative adversarial network. Experimental results show that our method is accurate and robust for predicting CT images from MRI images, and also outperforms three state-of-the-art methods under comparison.
A Two-Phase Approach Towards Identifying Argument Structure in Natural Language
Venue: arXiv cs.CL 2016
Year: 2016

We propose a new approach for extracting argument structure from natural language texts that contain an underlying argument. Our approach comprises of two phases: Score Assignment and Structure Prediction. The Score Assignment phase trains models to classify relations between argument units (Support, Attack or Neutral). To that end, different training strategies have been explored. We identify different linguistic and lexical features for training the classifiers. Through ablation study, we observe that our novel use of word-embedding features is most effective for this task. The Structure Prediction phase makes use of the scores from the Score Assignment phase to arrive at the optimal structure. We perform experiments on three argumentation datasets, namely, AraucariaDB, Debatepedia and Wikipedia. We also propose two baselines and observe that the proposed approach outperforms baseline systems for the final task of Structure Prediction.
ALPINE: A Bayesian System for Cloud Performance Diagnosis and Prediction
Venue: arXiv cs.DC 2016
Year: 2016

Cloud performance diagnosis and prediction is a challenging problem due to the stochastic nature of the cloud systems. Cloud performance is affected by a large set of factors including (but not limited to) virtual machine types, regions, workloads, wide area network delay and bandwidth. Therefore, necessitating the determination of complex relationships between these factors. The current research in this area does not address the challenge of building models that capture the uncertain and complex relationships between these factors. Further, the challenge of cloud performance prediction under uncertainty has not garnered sufficient attention. This paper proposes develops and validates ALPINE, a Bayesian system for cloud performance diagnosis and prediction. ALPINE incorporates Bayesian networks to model uncertain and complex relationships between several factors mentioned above. It handles missing, scarce and sparse data to diagnose and predict stochastic cloud performance efficiently. We validate our proposed system using extensive real data and trace-driven analysis and show that it predicts cloud performance with high accuracy of 91.93%.
A new cut-based genetic algorithm for graph partitioning applied to cell formation
Venue: arXiv cs.DM 2016
Year: 2016

Cell formation is a critical step in the design of cellular manufacturing systems. Recently, it was tackled using a cut-based-graph-partitioning model. This model meets real-life production systems requirements as it uses the actual amount of product flows, it looks for the suitable number of cells, and it takes into account the natural constraints such as operation sequences, maximum cell size, cohabitation and non-cohabitation constraints. Based on this model, we propose an original encoding representation to solve the problem by using a genetic algorithm. We discuss the performance of this new GA in comparison to some approaches taken from the literature on a set of medium sized instances. Given the results we obtained, it is reasonable to assume that the new GA will provide similar results for large real-life problems. Keywords: Group Technology, Manufacturing Cell Formation, Graph Partitioning, Graph Cuts, Genetic Algorithm, Encoding representation.
Efficient Encryption from Random Quasi-Cyclic Codes
Venue: arXiv cs.CR 2016
Year: 2016

We propose a framework for constructing efficient code-based encryption schemes from codes that do not hide any structure in their public matrix. The framework is in the spirit of the schemes first proposed by Alekhnovich in 2003 and based on the difficulty of decoding random linear codes from random errors of low weight. We depart somewhat from Aleknovich's approach and propose an encryption scheme based on the difficulty of decoding random quasi-cyclic codes. We propose two new cryptosystems instantiated within our framework: the Hamming Quasi-Cyclic cryptosystem (HQC), based on the Hamming metric, and the Rank Quasi-Cyclic cryptosystem (RQC), based on the rank metric. We give a security proof, which reduces the IND-CPA security of our systems to a decisional version of the well known problem of decoding random families of quasi-cyclic codes for the Hamming and rank metrics (the respective QCSD and RQCSD problems). We also provide an analysis of the decryption failure probability of our scheme in the Hamming metric case: for the rank metric there is no decryption failure. Our schemes benefit from a very fast decryption algorithm together with small key sizes of only a few thousand bits. The cryptosystems are very efficient for low encryption rates and are very well suited to key exchange and authentication. Asymptotically, for λthe security parameter, the public key sizes are respectively in $O(λ^{2})$ for HQC and in $O(λ^{4/3})$ for RQC. Practical parameter compares well to systems based on ring-LPN or the recent MDPC system.
Importance sampling-based approximate optimal planning and control
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, we propose a sampling-based planning and optimal control method of nonlinear systems under non-differentiable constraints. Motivated by developing scalable planning algorithms, we consider the optimal motion plan to be a feedback controller that can be approximated by a weighted sum of given bases. Given this approximate optimal control formulation, our main contribution is to introduce importance sampling, specifically, model-reference adaptive search algorithm, to iteratively compute the optimal weight parameters, i.e., the weights corresponding to the optimal policy function approximation given chosen bases. The key idea is to perform the search by iteratively estimating a parametrized distribution which converges to a Dirac's Delta that infinitely peaks on the global optimal weights. Then, using this direct policy search, we incorporated trajectory-based verification to ensure that, for a class of nonlinear systems, the obtained policy is not only optimal but robust to bounded disturbances. The correctness and efficiency of the methods are demonstrated through numerical experiments including linear systems with a nonlinear cost function and motion planning for a Dubins car.
Optimal Target Assignment and Path Finding for Teams of Agents
Venue: arXiv cs.AI 2016
Year: 2016

We study the TAPF (combined target-assignment and path-finding) problem for teams of agents in known terrain, which generalizes both the anonymous and non-anonymous multi-agent path-finding problems. Each of the teams is given the same number of targets as there are agents in the team. Each agent has to move to exactly one target given to its team such that all targets are visited. The TAPF problem is to first assign agents to targets and then plan collision-free paths for the agents to their targets in a way such that the makespan is minimized. We present the CBM (Conflict-Based Min-Cost-Flow) algorithm, a hierarchical algorithm that solves TAPF instances optimally by combining ideas from anonymous and non-anonymous multi-agent path-finding algorithms. On the low level, CBM uses a min-cost max-flow algorithm on a time-expanded network to assign all agents in a single team to targets and plan their paths. On the high level, CBM uses conflict-based search to resolve collisions among agents in different teams. Theoretically, we prove that CBM is correct, complete and optimal. Experimentally, we show the scalability of CBM to TAPF instances with dozens of teams and hundreds of agents and adapt it to a simulated warehouse system.
Dynamic Spectrum Leasing with Two Sellers
Venue: arXiv cs.GT 2016
Year: 2016

This paper studies dynamic spectrum leasing in a cognitive radio network. There are two spectrum sellers, who are two primary networks, each with an amount of licensed spectrum bandwidth. When a seller has some unused spectrum, it would like to lease the unused spectrum to secondary users. A coordinator helps to perform the spectrum leasing stage-by-stage. As the two sellers may have different leasing period, there are three epochs, in which seller 1 has spectrum to lease in Epochs II and III, while seller 2 has spectrum to lease in Epochs I and II. Each seller needs to decide how much spectrum it should lease to secondary users in each stage of its leasing period, with a target at revenue maximization. It is shown that, when the two sellers both have spectrum to lease (i.e., in Epoch II), the spectrum leasing can be formulated as a non-cooperative game. Nash equilibria of the game are found in closed form. Solutions of the two users in the three epochs are derived.
A Fusion Method Based on Decision Reliability Ratio for Finger Vein Verification
Venue: arXiv cs.CV 2016
Year: 2016

Finger vein verification has developed a lot since its first proposal, but there is still not a perfect algorithm. It is proved that algorithms with the same overall accuracy may have different misclassified patterns. We could make use of this complementation to fuse individual algorithms together for more precise result. According to our observation, algorithm has different confidence on its decisions but it is seldom considered in fusion methods. Our work is first to define decision reliability ratio to quantify this confidence, and then propose the Maximum Decision Reliability Ratio (MDRR) fusion method incorporating Weighted Voting. Experiment conducted on a data set of 1000 fingers and 5 images per finger proves the effectiveness of the method. The classifier obtained by MDRR method gets an accuracy of 99.42% while the maximum accuracy of the original individual classifiers is 97.77%. The experiment results also show the MDRR outperforms the traditional fusion methods as Voting, Weighted Voting, Sum and Weighted Sum.
Sparsity-Cognizant Multiple-Access Schemes for Large Wireless Networks With Node Buffers
Venue: arXiv cs.NI 2016
Year: 2016

This paper proposes efficient multiple-access schemes for large wireless networks based on the transmitters' buffer state information and their transceivers' duplex transmission capability. First, we investigate the case of half-duplex nodes where a node can either transmit or receive in a given time instant. The network is said to be naturally sparse if the number of nonempty-queue transmitters in a given frame is much smaller than the number of users, which is the case when the arrival rates to the queues are very small and the number of users is large. If the network is not naturally sparse, we design the user requests to be sparse such that only few requests are sent to the destination. We refer to the detected nonempty-queue transmitters in a given frame as frame owners. Our design goal is to minimize the nodes' total transmit power in a given frame. In the case of unslotted-time data transmission, the optimization problem is shown to be a convex optimization program. We propose an approximate formulation to simplify the problem and obtain a closed-form expression for the assigned time durations to the nodes. The solution of the approximate optimization problem demonstrates that the time duration assigned to a node in the set of frame owners is the ratio of the square-root of the buffer occupancy of that node to the sum of the square-roots of each occupancy of all the frame owners. We then investigate the slotted-time data transmission scenario, where the time durations assigned for data transmission are slotted. In addition, we show that the full-duplex capability of a node increases the data transmission portion of the frame and enables a distributed implementation of the proposed schemes.
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation
Venue: arXiv cs.IR 2016
Year: 2016

The increasing availability of implicit feedback datasets has raised the interest in developing effective collaborative filtering techniques able to deal asymmetrically with unambiguous positive feedback and ambiguous negative feedback. In this paper, we propose a principled kernel-based collaborative filtering method for top-N item recommendation with implicit feedback. We present an efficient implementation using the linear kernel, and we show how to generalize it to kernels of the dot product family preserving the efficiency. We also investigate on the elements which influence the sparsity of a standard cosine kernel. This analysis shows that the sparsity of the kernel strongly depends on the properties of the dataset, in particular on the long tail distribution. We compare our method with state-of-the-art algorithms achieving good results both in terms of efficiency and effectiveness.
Kleinberg's Grid Reloaded
Venue: arXiv cs.SI 2016
Year: 2016

One of the key features of small-worlds is the ability to route messages with few hops only using local knowledge of the topology. In 2000, Kleinberg proposed a model based on an augmented grid that asymptotically exhibits such property. In this paper, we propose to revisit the original model from a simulation-based perspective. Our approach is fueled by a new algorithm that uses dynamic rejection sampling to draw augmenting links. The speed gain offered by the algorithm enables a detailed numerical evaluation. We show for example that in practice, the augmented scheme proposed by Kleinberg is more robust than predicted by the asymptotic behavior, even for very large finite grids. We also propose tighter bounds on the performance of Kleinberg's routing algorithm. At last, we show that fed with realistic parameters, the model gives results in line with real-life experiments.
A new recurrent neural network based predictive model for Faecal Calprotectin analysis: A retrospective study
Venue: arXiv cs.LG 2016
Year: 2016

Faecal Calprotectin (FC) is a surrogate marker for intestinal inflammation, termed Inflammatory Bowel Disease (IBD), but not for cancer. In this retrospective study of 804 patients, an enhanced benchmark predictive model for analyzing FC is developed, based on a novel state-of-the-art Echo State Network (ESN), an advanced dynamic recurrent neural network which implements a biologically plausible architecture, and a supervised learning mechanism. The proposed machine learning driven predictive model is benchmarked against a conventional logistic regression model, demonstrating statistically significant performance improvements.
Distributed Data Processing Frameworks for Big Graph Data
Venue: arXiv cs.DC 2016
Year: 2016

Recently we create so much data (2.5 quintillion bytes every day) that 90% of the data in the world today has been created in the last two years alone [1]. This data comes from sensors used to gather traffic or climate information, posts to social media sites, photos, videos, emails, purchase transaction records, call logs of cellular networks, etc. This data is big data. In this report, we first briefly discuss what programming models are used for big data processing, and focus on graph data and do a survey study about what programming models/frameworks are used to solve graph problems at very large-scale. In section 2, we introduce the programming models which are not specifically designed to handle graph data but we include them in this survey because we believe these are important frameworks and/or there have been studies to customize them for more efficient graph processing. In section 3, we discuss some techniques that yield up to 1340 times speedup for some certain graph problems when applied to Hadoop. In section 4, we discuss vertex-based programming model which is simply designed to process large graphs and the frameworks adapting it. In section 5, we implement two of the fundamental graph algorithms (Page Rank and Weight Bipartite Matching), and run them on a single node as the baseline approach to see how fast they are for large datasets and whether it is worth to partition them.
3D Shape Induction from 2D Views of Multiple Objects
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we investigate the problem of inducing a distribution over three-dimensional structures given two-dimensional views of multiple objects taken from unknown viewpoints. Our approach called "projective generative adversarial networks" (PrGANs) trains a deep generative model of 3D shapes whose projections match the distributions of the input 2D views. The addition of a projection module allows us to infer the underlying 3D shape distribution without using any 3D, viewpoint information, or annotation during the learning phase. We show that our approach produces 3D shapes of comparable quality to GANs trained on 3D data for a number of shape categories including chairs, airplanes, and cars. Experiments also show that the disentangled representation of 2D shapes into geometry and viewpoint leads to a good generative model of 2D shapes. The key advantage is that our model allows us to predict 3D, viewpoint, and generate novel views from an input image in a completely unsupervised manner.
Learning a No-Reference Quality Metric for Single-Image Super-Resolution
Venue: arXiv cs.CV 2016
Year: 2016

Numerous single-image super-resolution algorithms have been proposed in the literature, but few studies address the problem of performance evaluation based on visual perception. While most super-resolution images are evaluated by fullreference metrics, the effectiveness is not clear and the required ground-truth images are not always available in practice. To address these problems, we conduct human subject studies using a large set of super-resolution images and propose a no-reference metric learned from visual perceptual scores. Specifically, we design three types of low-level statistical features in both spatial and frequency domains to quantify super-resolved artifacts, and learn a two-stage regression model to predict the quality scores of super-resolution images without referring to ground-truth images. Extensive experimental results show that the proposed metric is effective and efficient to assess the quality of super-resolution images based on human perception.